10 results about "Patient type" patented technology

The application discloses a kind of body cavity effusion pressure self-adaptive control drainage device, it is related to body cavity effusion technical field, including drainage tube, the top of drainage tube is fixedly connected with three-way joint;Liquid storage bag is connected to the export end of drainage tube;Pinch valve is set on drainage tube;Pressure sensor is set on three-way joint;Driving assembly is drivingly connected with pinch valve;Control box is electrically connected with pressure sensor and driving motor respectively, and control box is internally provided with microprocessor;And valve handle is set on pinch valve;By setting pressure sensor, control box, microprocessor and driving assembly, and control box is electrically connected with pressure sensor and driving motor respectively, while setting the setting panel electrically connected with microprocessor, so that medical staff can preset pressure safety range according to patient type, and pressure sensor monitors body cavity pressure in real time and feeds back to microprocessor.

The invention relates to a nursing simulation method and system, electronic equipment and a computer program product. The method comprises the following steps: determining a to-be-simulated case, and obtaining initial physiological parameters, a disease type and a patient type indicated by the to-be-simulated case; the physiological simulation human body is driven by a physiological engine to conduct state initialization according to the initial physiological parameters; acquiring professional knowledge corresponding to the disease type from a professional knowledge base through a dialogue engine, and acquiring personality description data corresponding to the patient type from a role knowledge base; when the voice data are collected, current physiological parameters are obtained from the physiological engine through the dialogue engine, and the current physiological parameters are matched with the current state of the physiological simulation human body; and calling a large language model to generate a reply text for the voice data based on professional knowledge, personality description data, voice data and current physiological parameters through a dialogue engine, and controlling a loudspeaker to perform voice playing according to the reply text. By adopting the method, the nursing simulation effect can be improved.

This invention discloses a hybrid model-driven nutritional risk prediction method and system for precision nutritional intervention. The method constructs a large-scale patient typing model containing multiple typical patient types and their associated assessment parameter packages, integrating a standardized first screening model with a personalized second screening model. In application, the first screening model is first used to standardize and initially screen patient data. Then, the large-scale patient typing model extracts patient feature inputs based on the patient data and initial screening results for typing and obtains the corresponding assessment parameter packages. The second screening model is then personalized based on these parameter packages, supplemented with key data, and combined with personalized thresholds to calculate the final risk level and generate a comprehensive assessment report. This invention achieves dynamic individualized adaptation of assessment strategies, improves the accuracy and clinical interpretability of risk identification, and optimizes workflow and resource allocation.

This application discloses an intelligent queuing method based on patient type, comprising the following steps: obtaining the attribute information of each patient in the current queue and calculating the static basic priority of each patient; calculating the dynamic hunger value of each patient based on the waiting time of each patient in the current queue; weighting the dynamic hunger value and the static basic priority of each patient to obtain the real-time priority of each patient; when a new patient needs to be enqueued, calculating the real-time priority of the new patient and determining the insertion position of the new patient in the current queue through an incremental local rearrangement algorithm based on the influence range. The beneficial effects of this application are: through the hunger value function, any patient with an excessively long waiting time can receive priority compensation, ensuring from the algorithm's principle that low-priority patients will not wait indefinitely, which aligns with human psychological expectations and reduces dissatisfaction caused by "being constantly cut in line"; the algorithm's parameters are adjustable and can adapt to various scenarios.

The invention relates to the technical field of lower limb rehabilitation training, in particular to an intelligent rehabilitation training method and system for lower limb exoskeletons. Comprising the steps of obtaining basic data of a first-level user, and generating a first-level rehabilitation strategy according to the basic data and a preset training optimization model; acquiring monitoring data of a first-level user, generating a feedback data packet, and generating a training deviation value according to the feedback data packet; judging whether a correction instruction is generated or not according to the training deviation value; by analyzing historical data, setting multiple training scenes based on different patient types and disease types, constructing quantitative standards of multiple training strategies, and dynamically adjusting an initial training plan in combination with doctor suggestions, accurate matching of individual rehabilitation requirements of patients and ability levels of different rehabilitation stages is realized, and the rehabilitation training efficiency is improved. The rehabilitation training efficiency is improved.

The invention relates to a diabetes adjuvant therapy cloud platform system based on an artificial intelligence model, and relates to the technical field of health-related information systems and cloud platforms, and the diabetes adjuvant therapy cloud platform system comprises a correlation analysis module which is used for determining a plurality of patient classification factors based on medical records of a plurality of historical diabetic patients, determining a plurality of diabetic patient types and the complication risk of each diabetic patient type; the feature extraction module is used for determining the type of the diabetic patient of the current patient according to the medical record of the current patient and the plurality of patient classification factors, and extracting complication risk features from the medical record of the current patient; the auxiliary treatment module is used for determining similar historical diabetic patients according to the complication risk characteristics of the current patient and determining an auxiliary treatment scheme of the current patient based on the auxiliary treatment scheme of the similar historical diabetic patients and the complication risk characteristics of the current patient, and the diabetes diagnosis and treatment efficiency and the treatment effect are improved.

The invention relates to the technical field of medical instruments, and discloses a respiratory system air leakage detection method for mechanical ventilation, which comprises the following steps: acquiring airway pressure, tidal volume and flow velocity waveforms in real time, performing multi-parameter fusion analysis, combining with an air leakage characteristic model constructed by machine learning to judge waveform abnormity, and dynamically adjusting a model judgment threshold according to the type of a patient; when the waveform is abnormal, three-stage progressive troubleshooting is started, the sealing performance of nine preset connecting points of a breathing machine pipeline is checked firstly, then the pressure of an air bag is monitored and adjusted to 30-50 cm H2O, dynamic minimum closing pressure monitoring is conducted, and finally the airway matching performance is evaluated; the method further comprises the steps of equipment fault verification and imaging examination prompt generation during suspected pneumothorax. The method improves the detection sensitivity and specificity, shortens the detection time, adapts to different patients, and reduces the risk of mechanical ventilation complications.

The application relates to the technical field of medical teaching, in particular to a virtual standardized patient teaching model generation method and system. The method comprises the following steps: acquiring case data for model training; analyzing the case data to determine patient inquiry conditions and patient types; calling inquiry site monitoring; based on the patient types, analyzing the inquiry site monitoring to determine the patient inquiry state of the same patient type; based on the patient types, analyzing the patient inquiry conditions to determine the patient interaction level; determining model training parameters according to the patient inquiry conditions, the patient inquiry state and the patient interaction level, and generating a virtual standardized patient teaching model according to the model training parameters. The virtual standardized patient teaching model improves the authenticity in the visual and auditory dimensions. The virtual standardized patient teaching model can accurately reflect the interaction dynamics in real inquiry. The creation of a high-fidelity teaching tool is realized, and inquiry training in a simulated environment is ensured.

This utility model discloses a patient information collection device for smart healthcare, belonging to the field of smart healthcare. It includes a base, a collection platform, and a baffle. The base is located at the bottom of the collection platform and the baffle. A viewing window is embedded in the baffle. Two holes are formed at the top of the base. Two driving components and a power mechanism are installed inside the base. The two driving components are located at the two holes, and the power mechanism is connected to the two driving components. The power mechanism provides power to the driving components, which in turn drive the patient's wheelchair. This smart healthcare patient information collection device, through the cooperation of the power mechanism and the driving components, achieves automatic driving for wheelchair users, eliminating the need for manual pushing and improving convenience and safety. The support component can extend and retract according to the patient type, providing sitting support for able-bodied patients while avoiding obstruction of wheelchair users' passage, thus improving the device's adaptability.

The invention relates to an emergency treatment management method, device and equipment and a computer readable storage medium. The method is applied to the technical field of medical treatment, and comprises the step of establishing a rapid identity confirmation mechanism by verifying the identity of a patient. This step ensures accurate acquisition of patient information. And secondly, disease condition grading judgment is carried out on the patients according to the preset diagnosis and treatment grading standard, and the types of the patients are determined based on the judgment result, so that the critical patients can be quickly identified and preferentially processed through the characteristic, the treatment timeliness is guaranteed, and the risk of disease condition delay is effectively avoided. In addition, the corresponding emergency treatment process is executed according to the type of the patient, so that medical resources are reasonably configured. The rescue efficiency of critical patients is ensured, the doctor-seeing experience of common emergency patients is optimized, and the orderliness of the whole diagnosis and treatment process is improved. And finally, carrying out expense settlement under the condition that the emergency treatment of the patient is finished, and integrating multiple dispersed payment links into a unified settlement process.